The global semiconductor industry is undergoing a structural reconfiguration driven by artificial intelligence, with the 3-nanometer process node emerging as the epicenter of this transformation. In this race, the relationship between NVIDIA and TSMC has transcended the traditional client-foundry boundary, evolving into a tightly coordinated strategic alliance—one commands architectural dominance in AI chips, while the other monopolizes the physical realization of advanced nodes. Together, they form a “bipolar power structure” underpinning today’s AI infrastructure, with 3nm capacity allocation serving as its most sensitive regulatory valve.
In the first four months of 2026, TSMC’s revenue surged by 30% year-over-year, with April alone generating $12.6 billion—the highest monthly figure in its history. This growth is almost entirely fueled by AI-related orders, particularly NVIDIA’s next-generation Blackwell Ultra and GB200 NVL72 systems built on 3nm. Industry sources indicate NVIDIA has secured over 60% of TSMC’s High-NA EUV capacity at its Fab 18 in Southern Taiwan Science Park for B200 and Grace CPU combo chips. This exclusivity is not merely commercial; it is strategic hoarding of scarce manufacturing resources.
Notably, despite NVIDIA’s market capitalization surpassing $4.8 trillion—making it the world’s most valuable company—its stock has consistently declined following its last three earnings reports. This reveals a deeper tension: market expectations have approached physical limits. Investors aren’t worried about demand; they fear supply bottlenecks—specifically, that 3nm capacity cannot keep pace with the exponential scaling of AI clusters. Although TSMC plans to launch its second U.S.-based 3nm fab in Arizona by late 2026, yield ramp-up and equipment calibration will take time. In the short term, 3nm production in Taiwan, China remains irreplaceable.
Geopolitics amplifies this fragility. While the U.S. pushes manufacturing reshoring via the CHIPS Act, building advanced 3nm lines requires more than just equipment like ASML’s High-NA EUV scanners—it demands decades of accumulated process know-how. Taiwan, China’s lead in this domain cannot be easily replicated by policy alone. During Trump’s first term, Washington pressured TSMC to relocate 3nm technology to the U.S., but ultimately failed to dislodge its domestic footprint—not due to lack of incentive, but because the ecosystem’s complexity defies simple transplantation.
Meanwhile, the explosion of the AI inference market is reshaping chip design logic. Training chips prioritize raw compute, while inference chips demand energy efficiency and cost-per-watt optimization. The 3nm node offers an optimal balance: 70% higher transistor density and 35% lower power consumption. NVIDIA is leveraging this advantage to migrate its L4 and L40S inference GPUs to 3nm, countering competition from AMD, Intel, and custom ASIC players. Yet all paths converge on the same bottleneck: TSMC’s 3nm capacity.
I judge that within the next 12 months, 3nm capacity will become the scarcest strategic asset in the AI industry. The deep integration between NVIDIA and TSMC not only fortifies the former’s technological moat but also cements the latter’s unassailable position in the global semiconductor value chain. However, this hyper-concentrated manufacturing model carries systemic risk: any geopolitical disruption or natural disaster affecting fabs in Taiwan, China could abruptly halt global AI deployment.
The more pressing question is this: when the engine of AI infrastructure hinges on a single region, a single process node, and two companies, is digital civilization building an overly fragile foundation? Decentralization is widely advocated, yet reality is accelerating concentration. A true breakthrough may not lie in constructing more fabs, but in developing a new computing paradigm capable of running large models efficiently without relying on 3nm—otherwise, the engine of the AI revolution will remain locked inside cleanrooms in the Hsinchu Science Park.